A Method of Spatial Mapping and Reclassification for High-Spatial-Resolution Remote Sensing Image Classification

Guizhou Wang,Guojin He,Jianbo Liu

doi:10.1155/2013/192982

Abstract

This paper presents a new classification method for high-spatial-resolution remote sensing images based on a strategic mechanism of spatial mapping and reclassification. The proposed method includes four steps. First, the multispectral image is classified by a traditional pixel-based classification method (support vector machine). Second, the panchromatic image is subdivided by watershed segmentation. Third, the pixel-based multispectral image classification result is mapped to the panchromatic segmentation result based on a spatial mapping mechanism and the area dominant principle. During the mapping process, an area proportion threshold is set, and the regional property is defined as unclassified if the maximum area proportion does not surpass the threshold. Finally, unclassified regions are reclassified based on spectral information using the minimum distance to mean algorithm. Experimental results show that the classification method for high-spatial-resolution remote sensing images based on the spatial mapping mechanism and reclassification strategy can make use of both panchromatic and multispectral information, integrate the pixel- and object-based classification methods, and improve classification accuracy.

Highlights

With the development of improved sensors and powerful computation technology, high-spatial-resolution remote sensing data have been more acquired and widely applied [1]
This paper proposes a new high-spatial-resolution remote sensing image classification method based on a mechanism of spatial mapping and a strategy of reclassification
The object-oriented Support vector machine (SVM) was applied on the panchromatic segmentation regions and the region feature was computed from the fusion images

Summary

Introduction

With the development of improved sensors and powerful computation technology, high-spatial-resolution remote sensing data have been more acquired and widely applied [1]. High-spatial-resolution remote sensing images contain more information and have increased the detail at which earth observations can be made. One challenge is that traditional image classification technology can no longer satisfy the needs of high-spatial-resolution remote sensing image classification. High-spatial-resolution remote sensing imagery, such as SPOT-5, IKONOS, and QuickBird, has been used in many fields in recent years [2]. They have been applied for urban planning, urban change detection, tree canopy mapping, ecological environment monitor, precision agriculture, and so forth [3]. The extraction of geographical information from a high-spatial-resolution satellite image is topical [2]

Methods

Results

Conclusion